Research on the detection of concrete surface cracks based on U2net neural network
An improved network AM-U2net based on U2net neural network is proposed to achieve more accurate and rapid detection of concrete cracks.Firstly,the attention mechanism is applied to the skip connection of the original U2net network.CBAM attention module and ECA attention module are added to the upper and lower layers of the network structure respectively.Then,an AM-U2net network is proposed after introducing Mish activation function.The results show that the per-formance of AM-U2net is superior to the comparison networks Unet,Unet++and the original U2net,with accuracy,precision,recall and f1score reaching 99.1%,64.2%,65.4%and 64.8%re-spectively.The concrete crack prediction image output by the model is based on the skeleton line method.The length and width attributes of the cracks are marked and calculated at the pixel level,so that the model's predicted crack results can reflect some basic properties of concrete cracks.The im-proved algorithm proposed in this study can effectively identify concrete cracks,providing ideas for subsequent research.
concrete cracksU2netattention mechanismactivation functionskeleton line
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